Last but not least, in the quest of `map-less' navigation, task-driven maps emerge as a novel map representation. This representation is jointly modelled by deep neural networks with respect to the task at hand. Generally those tasks leverage location information, such as navigation or path planning, requiring mobile agents to understand the geometry and semantics of environment.
Navigation in unstructured environments (even in a city scale) is formulated as an policy learning problem in these works [1]}, [2]}, [3]}, [4]}, and solved by deep reinforcement learning.
Different from traditional solutions that follow a procedure of building an explicit map, planning path and making decisions, these learning based techniques predict control signals directly from sensor observations in an end-to-end manner, without explicitly modelling the environment. The model parameters are optimized via sparse reward signals, for example, whenever agents reach a destination, a positive reward will be given to tune the neural network.
Once a model is trained, the actions of agents can be determined conditioned on the current observations of environment, i.e. images. In this case, all of environmental factors, such as the geometry, appearance and semantics of a scene, are embedded inside the neurons of a deep neural network and suitable for solving the task at hand.
Interestingly, the visualization of the neurons inside a neural model that is trained on the navigation task via reinforcement learning, has similar patterns as the grid and place cells inside human brain. This provides cognitive cues to support the effectiveness of neural map representation.
